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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Rosin, Paul L. (2010)
Publisher: Elsevier
Languages: English
Types: Article
Subjects: QA75
This paper describes the application of cellular automata (CA) to various image processing tasks such as denoising and feature detection. Whereas our previous work mainly dealt with binary images, the current work operates on intensity images. The increased number of cell states (i.e. pixel intensities) leads to a vast increase in the number of possible rules. Therefore, a reduced intensity representation is used, leading to a three state CA that is more practical. In addition, a modified sequential floating forward search mechanism is developed in order to speed up the selection of good rule sets in the CA training stage. Results are compared with our previous method based on threshold decomposition, and are found to be generally superior. The results demonstrate that the CA is capable of being trained to perform many different tasks, and that the quality of these results is in many cases comparable or better than established specialised algorithms.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] I. Kusch and M. Markus. Mollusc shell pigmentation: cellular automaton simulations and evidence for undecidability. J. Theor. Biol., 178:333-340, 1996.
    • [2] P.P. Chaudhuri, D.R. Chowdhury, S. Nandi, and S. Chattopadhyay. Theory and Applications: Additive Cellular Automata. IEEE Press, 1997.
    • [3] C. Georgoulas, L. Kotoulas, and G. Sirakoulis. Real-time disparity map computation module. J. Microprocessors and Microsystems, 32(3):159-170, 2008.
    • [4] M. Mitchell, P.T. Hraber, and J.P. Crutchfield. Evolving cellular automata to perform computation: Mechanisms and impedients. Physica D, 75:361-391, 1994.
    • [5] P. Sahota, M.F. Daemi, and D.G. Elliman. Training genetically evolving cellular automata for image processing. Proc. Speech, Image Processing and Neural Networks, 2:753-756, 1994.
    • [6] A. Adamatzky. Automatic programming of cellular automata: identification approach. Kybernetes, 26(2):126-135, 1997.
    • [7] M. Batouche, S. Meshoul, and A. Abbassene. On solving edge detection by emergence. In Int. Conf. on Industrial, Engineering and Other Apps. of Applied Intelligent Systems, volume LNAI 4031, pages 800-808, 2006.
    • [8] S. Slatnia, M. Batouche, and K.E. Melkemi. Evolutionary cellular automata based-approach for edge detection. In Int. Workshop on Fuzzy Logic and Applications, volume LNAI 4578, pages 404-411, 2007.
    • [9] A. Chavoya and Y. Duthen. Using a genetic algorithm to evolve cellular automata for 2D/3D computational development. In Genetic and Evolutionary Comp. Conf., pages 231-232, 2006.
    • [10] R.V. Craiu and T.C.M. Lee. Pattern generation using likelihood inference for cellular automata. IEEE Trans. on Image Processing, 15(7):1718-1727, 2006.
    • [11] S.A. Billings and S.S. Mei. A new fast cellular automata orthogonal least-squares identification method. Int. J. Systems Science, 36(8):491-499, 2005.
    • [12] S.A. Billings and Y. Yang. Identification of the neighborhood and CA rules from spatiotemporal CA patterns. IEEE Trans. on Systems, Man and Cybernetics, Part B, 33(2):332- 339, 2003.
    • [13] L. Bull and A. Adamatzky. A learning classifier system approach to the identification of cellular automata. J. Cellular Automata, 2(1):21-38, 2007.
    • [14] G. Terrazas, P. Siepmann, G. Kendall, and N.O. Krasnogor. An evolutionary methodology for the automated design of cellular automaton-based complex systems. J. Cellular Automata, 2(1):77-102, 2007.
    • [15] B. Straatman, R. White, and G. Engelen. Towards an automatic calibration procedure for constrained cellular automata. Computers, Environment and Urban Systems, 28(1-2):149- 170, 2004.
    • [16] K.I. Maeda and C. Sakama. Identifying cellular automata rules. J. Cellular Automata, 2(1):1-20, 2007.
    • [17] P.L. Rosin. Training cellular automata for image processing. IEEE Trans. on Image Processing, 15(7):2076-2087, 2006.
    • [18] F.S. Roberts and B. Tesman. Applied Combinatorics. Pearson/Prentice-Hall, 2005.
    • [19] L. Wang and D.C. He. Texture classification using texture spectrum. Pattern Recognition, 23:905-910, 1990.
    • [20] P. Pudil, J. Novovicova, and J.V. Kittler. Floating search methods in feature-selection. Pattern Recognition Letters, 15(11):1119-1125, 1994.
    • [21] T. Ojala, M. Pietik¨ainen, and T. M¨aenp¨a¨a. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(7):971-987, 2002.
    • [22] Z. Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. on Image Processing, 13(4):600-612, 2004.
    • [23] Z. Wang and A. C. Bovik. A universal image quality index. IEEE Signal Processing Letters, 9(3):81-84, 2002.
    • [24] L. Khriji and M. Gabbouj. Median-rational hybrid filters for image restoration. Electronic Letters, 34(10):977-979, 1998.
    • [25] H. Hwang and R.A. Haddad. Adaptive median filters: new algorithms and results. IEEE Trans. on Image Processing, 4(4):499-502, 1995.
    • [26] J. Romberg, H. Choi, and R. Baraniuk. Bayesian tree-structured image modeling using wavelet domain hidden markov models. IEEE Trans. on Image Processing, 10(7):1056-1068, 2001.
    • [27] M.S. Crouse, R.D. Nowak, and R.G. Baraniuk. Wavelet-based statistical signal-processing using hidden markov-models. IEEE Trans. on Signal Processing, 46(4):886-902, 1998.
    • [28] G. Gilboa, N.A. Sochen, and Y.Y. Zeevi. Image enhancement and denoising by complex diffusion processes. IEEE Trans. on Pattern Analysis and Machine Intelligence, 26(8):1020- 1036, 2004.
    • [29] L. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.
    • [30] A. Meijster and M. Wilkinson. A comparison of algorithms for connected set openings and closings. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24(4):484-494, 2002.
    • [31] W.H. Tsai. Moment-preserving thresholding. CVGIP, 29:377-393, 1985.
    • [32] C. Steger. An unbiased detector of curvilinear structures. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(2):113-125, 1998.
    • [33] P. Soille. Grey scale convex hulls: definition, implementation, and application. In H. Heijmans and J. Roerdink, editors, Mathematical Morphology and its Applications to Image and Signal Processing, volume 12, pages 83-90. Kluwer Academic Publishers, 1998.
    • [34] I. Nystr¨om, G. Borgefors, and G. Sanniti di Baja. 2D grey-level convex hull computation: A discrete 3D approach. In Proc. Scand. Conf. Image Anal., volume 2749 of LNCS, pages 763-770. Springer, 2003.
    • [37] S. V´erel, P. Collard, M. Tomassini, and L. Vanneschi. Fitness landscape of the cellular automata majority problem: View from the 'Olympus'. Theor. Comput. Sci, 378(1):54-77, 2007.
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